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The FELM-AE introduces the Fisher criterion into the ELM-AE by adding the Fisher regularization term to the objective function, aiming to maximize the between-class distance and minimize the within-class distance of abstract feature. Different from the ELM-AE, the FELM-AE requires class labels to calculate the Fisher regularization loss, so that the learned abstract feature contains sufficient category information to complete classification tasks. The ML-FELM stacks the FELM-AE to extract feature and adopts the extreme leaning machine (ELM) to classify samples. Experiments on benchmark datasets show that the abstract feature extracted by the FELM-AE is more discriminative than the ELM-AE, and the classification results of the ML-FELM are more competitive and robust in comparison with the ELM, one-dimensional convolutional neural network (1D-CNN), ML-ELM, denoising multilayer extreme learning machine (D-ML-ELM), multilayer generalized extreme learning machine (ML-GELM), and hierarchical extreme learning machine with L21\u2011norm loss and regularization (H-LR21-ELM).<\/jats:p>","DOI":"10.1007\/s40747-022-00867-7","type":"journal-article","created":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T08:04:06Z","timestamp":1667203446000},"page":"1975-1993","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multilayer Fisher extreme learning machine for classification"],"prefix":"10.1007","volume":"9","author":[{"given":"Jie","family":"Lai","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2785-9539","authenticated-orcid":false,"given":"Xiaodan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Xiang","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Lei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"issue":"1","key":"867_CR1","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. 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